MolecRank: A Specificity-Based Network Analysis Algorithm

Ranking Therapeutic Molecules in the Bibliome
  • Ahmed Abdeen HamedEmail author
  • Agata Leszczynska
  • Mark Schreiber
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


Biomedical scientists often search databases of therapeutic molecules to answer a set of drug-related queries. In this paper, we present a novel network algorithm called MolecRank that is specialized in searching and ranking molecules using a biomedical literature. Starting with a disease-related set of publications (e.g., depression), a feature extraction step is performed to identify the biological features associated with the drugs of study. The MolecRank is a network centrality algorithm that is specialized in deriving a rank when specificity is in question. The algorithm’s promise is two folds (a) an interesting search-and-rank tool that demonstrated its importance in the drug discovery research, (b) a theoretical network centrality measure that is based on the notion of specificity. We performed our experiments against a depression-related literature dataset. The results shows an interesting order that is significantly different from well-advertised drugs (e.g., Cymbalta#10 though well-advertised). We conclude that not all well-advertised drugs are most specific. This striking evidence highlights the significance of specificity as an important measure in discovering new drugs.


Specificity centrality Ranking algorithms Therapeutic molecules Literature mining 



The authors would like thank Greg Temsi, Ramiro Barrantes for their valuable discussions. The authors also greatly appreciate the tremendous feedback on this work giving by Dr. Barabasi and his lab members. We also thank Dr. Karin Verspoor of University of Melbourne for the valuable discussions.


  1. 1.
    Bragazzi, N.L., Nicolini, C.: A leader genes approach-based tool for molecular genomics: from gene-ranking to gene-network systems biology and biotargets predictions. J. Comput. Sci. Syst. Biol. 6, 165–176 (2013)CrossRefGoogle Scholar
  2. 2.
    Winter, C., Kristiansen, G., Kersting, S., Roy, J., Aust, D., Knösel, T., Rümmele, P., Jahnke, B., Hentrich, V., Rückert, F., Niedergethmann, M., Weichert, W., Bahra, M., Schlitt, H.J., Settmacher, U., Friess, H., Büchler, M., Saeger, H.-D., Schroeder, M., Pilarsky, C., Grützmann, R.: Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes. PLOS Comput. Biol. 8(5), 1–16 (2012)CrossRefGoogle Scholar
  3. 3.
    Weston, J., Elisseeff, A., Zhou, D., Leslie, C.S., Noble, W.S.: Protein ranking: from local to global structure in the protein similarity network. Proc. Nat. Acad. Sci. U. S. A. 101(17), 6559–6563 (2004)CrossRefGoogle Scholar
  4. 4.
    Wren, J.D., Garner, H.R.: Shared relationship analysis: ranking set cohesion and commonalities within a literature-derived relationship network. Bioinformatics 20(2), 191–198 (2004)CrossRefGoogle Scholar
  5. 5.
    Chen, J., Jagannatha, A.N., Fodeh, S.J., Yu, H.: Ranking medical terms to support expansion of lay language resources for patient comprehension of electronic health record notes: adapted distant supervision approach. JMIR Med. Inform. 5(4), e42 (2017)CrossRefGoogle Scholar
  6. 6.
    Koschützki, D., Schwöbbermeyer, H., Schreiber, F.: Ranking of network elements based on functional substructures. J. Theoret. Biol. 248(3), 471–479 (2007)CrossRefGoogle Scholar
  7. 7.
    Junker, B.H., Koschützki, D., Schreiber, F.: Exploration of biological network centralities with CentiBIN. BMC Bioinform. 7(1), 219 (2006)CrossRefGoogle Scholar
  8. 8.
    Hopkins, A.L.: Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4(11), 682–690 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Bodnarchuk, M.S., Heyes, D.M., Dini, D., Chahine, S., Edwards, S.: Role of deprotonation free energies in pKa prediction and molecule ranking. J. Chem. Theory Comput. 10(6), 2537–2545 (2014)CrossRefGoogle Scholar
  10. 10.
    Koshland, D.E.: Application of a theory of enzyme specificity to protein synthesis. Proc. Nat. Acad. Sci. 44(2), 98–104 (1958)CrossRefGoogle Scholar
  11. 11.
    Lehninger, A., Nelson, D.L., Cox, M.M.: Lehninger Principles of Biochemistry, 5th edn. W. H. Freeman, San Francisco (2008)Google Scholar
  12. 12.
    Wood, E.J.: Harper’s Biochemistry 24th edition by R.K. Murray, D.K. Granner, P.A. Mayes and V.W Rodwell. pp 868. Appleton & Lange, Stamford, CT (1996). £ 28.95 isbn 0-8385-3612-3. Biochem. Educ. 24(4), 237–237 (1996)Google Scholar
  13. 13.
    Hu, L., Fawcett, J.P., Gu, J.: Protein target discovery of drug and its reactive intermediate metabolite by using proteomic strategy. Acta Pharm. Sin. B 2(2), 126–136 (2012)CrossRefGoogle Scholar
  14. 14.
    Hefti, F.F.: Requirements for a lead compound to become a clinical candidate. BMC Neurosci. 9(3), S7 (2008)CrossRefGoogle Scholar
  15. 15.
    Degterev, A., Maki, J.L., Yuan, J.: Activity and specificity of necrostatin-1, small-molecule inhibitor of rip1 kinase. Cell Death Differ. 20(2), 366 (2013)CrossRefGoogle Scholar
  16. 16.
    Eaton, B.E., Gold, L., Zichi, D.A.: Let’s get specific: the relationship between specificity and affinity. Chem. Biol. 2(10), 633–638 (1995)CrossRefGoogle Scholar
  17. 17.
    Radhakrishnan, M.L., Tidor, B.: Specificity in molecular design: a physical framework for probing the determinants of binding specificity and promiscuity in a biological environment. J. Phys. Chem. B 111(47), 13419–13435 (2007)CrossRefGoogle Scholar
  18. 18.
    Strovel, J., Sittampalam, S., Coussens, N.P., Hughes, M., Inglese, J., Kurtz, A., Andalibi, A., Patton, L., Austin, C., Baltezor, M., et al.: Early drug discovery and development guidelines: for academic researchers, collaborators, and start-up companies (2016)Google Scholar
  19. 19.
    Hartley, J.A., Lown, J.W., Mattes, W.B., Kohn, K.W.: Dna sequence specificity of antitumor agents: oncogenes as possible targets for cancer therapy. Acta Oncol. 27(5), 503–510 (1988)CrossRefGoogle Scholar
  20. 20.
    Timchenko, L.T., Timchenko, N.A., Caskey, C.T., Roberts, R.: Novel proteins with binding specificity for DNA CTG repeats and RNA CUG repeats: implications for myotonic dystrophy. Hum. Mol. Genet. 5(1), 115–121 (1996)CrossRefGoogle Scholar
  21. 21.
    Settles, B.: ABNER: an open source tool for automatically tagging genes, proteins, and other entity names in text. Bioinformatics 21(14), 3191–3192 (2005)CrossRefGoogle Scholar
  22. 22.
    Carpenter, B.: Lingpipe for 99.99% recall of gene mentions. In: Proceedings of the Second BioCreative Challenge Evaluation Workshop, vol. 23, pp. 307–309 (2007)Google Scholar
  23. 23.
    Candan, K.S., Liu, H., Suvarna, R.: Resource description framework: metadata and its applications. SIGKDD Explor. Newsl. 3(1), 6–19 (2001)CrossRefGoogle Scholar
  24. 24.
    Shannon, C.E.: Prediction and entropy of printed English. Bell Labs Tech. J. 30(1), 50–64 (1951)CrossRefGoogle Scholar
  25. 25.
    Koschützki, D., Schreiber, F.: Centrality analysis methods for biological networks and their application to gene regulatory networks. Gene Regul. Syst. Biol. 2, 193 (2008)Google Scholar
  26. 26.
    Jeong, H., Mason, S.P., Barabási, A.-L., Oltvai, Z.N.: Lethality and centrality in protein networks. Nature 411(6833), 41–42 (2001)CrossRefGoogle Scholar
  27. 27.
    Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O.: Centrality Indices, pp. 16–61. Springer, Berlin (2005)zbMATHGoogle Scholar
  28. 28.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)CrossRefGoogle Scholar
  29. 29.
    Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: generalizing degree and shortest paths. Soc. Netw. 32(3), 245–251 (2010)CrossRefGoogle Scholar
  30. 30.
    Zhou, Q., Womer, F.Y., Kong, L., Wu, F., Jiang, X., Zhou, Y., Wang, D., Bai, C., Chang, M., Fan, G., et al.: Trait-related cortical-subcortical dissociation in bipolar disorder: analysis of network degree centrality. J. Clin. Psychiatry 78(5), 584–591 (2017)CrossRefGoogle Scholar
  31. 31.
    Costenbader, E., Valente, T.W.: The stability of centrality measures when networks are sampled. Soc. Netw. 25(4), 283–307 (2003)CrossRefGoogle Scholar
  32. 32.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)Google Scholar
  33. 33.
    Pretto, L.: A theoretical analysis of google’s pagerank. In: String Processing and Information Retrieval, pp. 125–136. Springer (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ahmed Abdeen Hamed
    • 1
    Email author
  • Agata Leszczynska
    • 2
  • Mark Schreiber
    • 1
    • 3
  1. 1.Merck & Co., Inc.BostonUSA
  2. 2.MSDPragueCzech Republic
  3. 3.Kaleido BiosciencesBedfordUSA

Personalised recommendations